CN113188231B - Liquid-carrying characteristic extraction method and device for compressor of air conditioner, storage medium and air conditioner - Google Patents
Liquid-carrying characteristic extraction method and device for compressor of air conditioner, storage medium and air conditioner Download PDFInfo
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- CN113188231B CN113188231B CN202110494438.XA CN202110494438A CN113188231B CN 113188231 B CN113188231 B CN 113188231B CN 202110494438 A CN202110494438 A CN 202110494438A CN 113188231 B CN113188231 B CN 113188231B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
Abstract
The invention provides a method and a device for extracting liquid-carrying characteristics of a compressor of an air conditioner, a storage medium and the air conditioner, wherein the method comprises the following steps: determining whether the air conditioner is in a defrosting mode when the air conditioner is in heating operation; when the air conditioner is determined to be in the defrosting mode, sampling the phase current of the compressor of the air conditioner to obtain a phase current signal of the compressor; performing wavelet analysis on the sampled compressor phase current signals to extract the liquid-carrying characteristics of the compressor in the compressor phase current signals; and acquiring the suction dryness of the compressor according to the extracted compressor liquid-carrying characteristics in the phase current signal of the compressor. The scheme provided by the invention can realize online real-time recognition of the suction dryness of the compressor.
Description
Technical Field
The invention relates to the field of control, in particular to a method and a device for extracting liquid-carrying characteristics of a compressor of an air conditioner, a storage medium and the air conditioner.
Background
In air conditioning systems, the problem of low temperature heating has been a pain point that affects comfort. Particularly in Yangtze river basin, the humidity is high in winter, an outdoor condenser of the air conditioner is easy to frost, and the system needs frequent defrosting. This will inevitably affect the indoor temperature, causing large fluctuations in the indoor temperature, affecting comfort.
In the defrosting process of the air conditioner outdoor unit, the most important system parameter is the suction dryness, and the value influences the heat generated by the work of the compressor during defrosting. The suction dryness is taken as a control target, and when the compressor is in a slight liquid-carrying state, the compressor can realize the maximum work performance. However, if the amount of liquid carried is too large, the compressor may be subjected to liquid impact, so that the wear of the compressor is accelerated if the amount of liquid carried is too large, and parts of the compressor are damaged if the amount of liquid carried is too large, thereby seriously affecting the reliability of the whole machine. Therefore, how to accurately identify the dryness of the suction gas of the compressor, i.e. the degree of liquid entrainment, is a technical problem to be solved urgently at present.
The suction dryness is used as a state characteristic of the refrigerant, and the identification process is difficult. The traditional air suction dryness identification method adopts numerical calculation of system operation parameters such as air suction temperature, exhaust temperature, air suction pressure, exhaust pressure and the like of an air conditioning system. Due to the hysteresis of the temperature, the calculation is often required to be performed when the system parameters are stable, and the real-time calculation and analysis are difficult to realize.
Disclosure of Invention
The main purpose of the present invention is to overcome the above-mentioned defects in the prior art, and to provide a method and an apparatus for extracting the liquid-carrying characteristics of a compressor of an air conditioner, a storage medium, and an air conditioner, so as to solve the problem that the liquid-carrying characteristics of the compressor are difficult to monitor in real time in the prior art.
The invention provides a method for extracting liquid-carrying characteristics of a compressor of an air conditioner, which comprises the following steps: determining whether the air conditioner is in a defrosting mode when the air conditioner is in heating operation; when the air conditioner is determined to be in the defrosting mode, sampling the phase current of the compressor of the air conditioner to obtain a phase current signal of the compressor; and performing wavelet analysis on the sampled compressor phase current signals to extract the liquid-carrying characteristics of the compressor in the compressor phase current signals.
Optionally, the method further comprises: filtering the sampled compressor phase current signals to filter out sampling interference in the compressor phase current signals; wavelet analysis is carried out on the sampled compressor phase current signals, and the wavelet analysis comprises the following steps: and performing wavelet analysis on the compressor phase current signal obtained after filtering.
Optionally, performing wavelet analysis on the sampled compressor phase current signal to extract a fluid-carrying feature in the compressor phase current signal, including: wavelet analysis is carried out on the sampled phase current signals of the compressor, and the characteristic frequency of the compressor with liquid is extracted; and carrying out Fourier transform based on the extracted compressor liquid-carrying characteristic frequency to obtain an amplitude corresponding to the compressor liquid-carrying characteristic frequency.
Optionally, performing wavelet analysis on the sampled compressor phase current signal, including: setting analysis parameters and analysis frequency range required by wavelet analysis; obtaining a wavelet basis function for performing wavelet analysis on the phase current signal of the compressor according to set analysis parameters; and performing wavelet packet decomposition of a target order on the sampled compressor phase current signal according to the obtained wavelet basis function and the analysis frequency range, and extracting the compressor liquid-carrying characteristic frequency.
Optionally, the wavelet basis functions include:
wherein a is a scale factor, b is a translation factor,
f is the signal frequency corresponding to the scale factor a of the wavelet transform, j belongs to Z, a0And > 1, and gamma is a correction coefficient.
In another aspect, the present invention provides a liquid-carrying characteristic extracting apparatus for a compressor of an air conditioner, including: a determination unit for determining whether the air conditioner is in a defrosting mode when the air conditioner is in a heating operation; the sampling unit is used for sampling the phase current of the compressor of the air conditioner to obtain a phase current signal of the compressor when the determining unit determines that the air conditioner is in the defrosting mode; and the analysis unit is used for performing wavelet analysis on the compressor phase current signals sampled by the sampling unit so as to extract the liquid-carrying characteristics of the compressor in the compressor phase current signals.
Optionally, the method further comprises: the filtering unit is used for filtering the sampled compressor phase current signals so as to filter out sampling interference in the compressor phase current signals; the analysis unit is used for performing wavelet analysis on the sampled compressor phase current signals and comprises the following steps: and performing wavelet analysis on the compressor phase current signal obtained after filtering.
Optionally, the analyzing unit performs wavelet analysis on the sampled compressor phase current signal to extract a fluid-carrying feature in the compressor phase current signal, and the analyzing unit includes: the wavelet analysis subunit is used for performing wavelet analysis on the sampled compressor phase current signals and extracting the compressor liquid-carrying characteristic frequency; and the Fourier transform subunit is used for carrying out Fourier transform on the extracted compressor liquid-carrying characteristic frequency to obtain an amplitude corresponding to the compressor liquid-carrying characteristic frequency.
Optionally, the wavelet analysis subunit performs wavelet analysis on the sampled compressor phase current signal, and includes: setting analysis parameters and analysis frequency range required by wavelet analysis; determining a wavelet basis function for performing wavelet analysis on the phase current signal of the compressor according to the set analysis parameters; and performing wavelet packet decomposition of a target order on the sampled compressor phase current signal according to the obtained wavelet basis function and the analysis frequency range, and extracting the compressor liquid-carrying characteristic frequency.
Optionally, the wavelet basis functions include:
wherein a is a scale factor, b is a translation factor,
f is the signal frequency corresponding to the scale factor a of the wavelet transform, j belongs to Z, a0And > 1, and gamma is a correction coefficient.
A further aspect of the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of any of the methods described above.
Yet another aspect of the present invention provides an air conditioner comprising a processor, a memory, and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of any of the methods described above when executing the program.
The invention further provides an air conditioner which comprises the compressor liquid-carrying characteristic extraction device.
According to the technical scheme of the invention, based on wavelet analysis technology, the liquid-carrying characteristic quantity of the compressor is extracted, so that the suction dryness of the compressor is identified; an analysis method suitable for extracting liquid-carrying characteristics of phase current of the compressor is constructed, online recognition of the suction dryness of the compressor is realized, and a technical basis is provided for realizing optimal suction dryness control. The method is based on the wavelet analysis technology, fully considers the liquid-carrying characteristic characteristics of phase current, improves the wavelet basis function, and has better information extraction capability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a method for extracting liquid-carrying characteristics of a compressor according to an embodiment of the present invention;
FIG. 2a is a graph of phase current waveforms during a suction superheat compression regime;
FIG. 2b is a graph of phase current waveforms in a slightly wet compressed state;
FIG. 2c is a graph of phase current waveforms for a severe fluid compression regime;
FIG. 3 is a flow chart of the analog-to-digital conversion process for the sampled compressor phase current signals;
FIG. 4 is a flowchart of one embodiment of the step of performing wavelet analysis on sampled compressor phase current signals to extract compressor flooded characteristics from the compressor phase current signals;
FIG. 5 is a flowchart of an embodiment of the step of extracting the compressor fluid-carrying characteristic frequency by wavelet analysis of the sampled compressor phase current signal;
FIG. 6 is a schematic exploded view of a 3-layer complete wavelet packet binary tree;
FIG. 7 is a flow chart of data analysis of compressor phase current signals;
FIG. 8 is a functional block diagram of software according to the present invention;
FIG. 9 is a flow chart of the main control module;
fig. 10 is a block diagram illustrating an embodiment of a liquid-carrying characteristic extracting apparatus for a compressor of an air conditioner according to the present invention;
FIG. 11 is a block diagram of an embodiment of an analysis unit according to the present invention;
fig. 12 is a schematic structural diagram of an embodiment of a high defrosting heat control device based on multi-dimensional coupling.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The operation state of the compressor is reflected in its phase current, which generates a large range of fluctuation, especially when the compressor is subjected to liquid impact. For example, during operation of the compressor, a load condition may be exhibited in the phase currents. When the compressor is slightly liquid-carrying, the electromagnetic torque of the compressor generates certain pulsation along with the compressor; when the liquid is heavily entrained, there is also a fluctuation in the operating frequency. For example, the compressor fluid state is divided into a suction superheat compression state, a slight wet compression state and a severe fluid-carrying compression state, and the phase current waveforms of the compressor in the different fluid-carrying states are compared and can be seen in fig. 2a, 2b and 2 c. FIG. 2a is a graph of phase current waveforms during a suction superheat compression regime; FIG. 2b is a graph of phase current waveforms in a slightly wet compressed state; fig. 2c is a diagram of phase current waveforms in a heavily fluid-compressed state.
As shown in fig. 2a, the suction superheat compression regime: the phase current waveform is not phase shifted and the frequency ripple is minimal. As shown in fig. 2b, slightly wet compressed state: the phase current waveform is slightly phase shifted and the frequency is slightly irregularly pulsed. As shown in fig. 2c, a severely fluid-filled compressed state: the phase shift of the phase current waveform is serious, and the operation frequency is irregular and pulsated and fluctuates intermittently.
Therefore, the phase current of the compressor can be used as a basic parameter for identifying the dryness of the intake air. The characteristic analysis for phase currents can generally employ fourier decomposition, but for the liquid-carrying characteristics, fourier decomposition cannot extract local characteristics of the current waveform.
The invention provides a method for extracting liquid-carrying characteristics of a compressor, which is used for extracting the liquid-carrying characteristics of the compressor based on a wavelet analysis technology so as to identify the dryness of suction gas.
Fig. 1 is a schematic method diagram of an embodiment of a method for identifying characteristics of liquid carried in a compressor according to the present invention.
As shown in fig. 1, according to an embodiment of the present invention, the method for identifying the liquid-carrying characteristics of the compressor at least includes step S110, step S120 and step S130.
And step S110, determining whether the air conditioner is in a defrosting mode when the air conditioner is in heating operation.
For example, when the air conditioner is in heating operation, the operation parameters of the air conditioner are obtained, and whether the air conditioner is in a defrosting mode or not can be determined according to the operation parameters of the air conditioner.
And step S120, when the air conditioner is determined to be in the defrosting mode, sampling the phase current of the compressor of the air conditioner to obtain a phase current signal of the compressor.
For example, the compressor phase current may be sampled by a current sensor to obtain a compressor phase current signal. Optionally, the sampled compressor phase current signal may be subjected to filtering processing to filter out sampling interference in the compressor phase current signal. Optionally, analog-to-digital conversion processing may be performed on the sampled compressor phase current signal to obtain a digital quantity of the compressor phase current.
Referring to fig. 3, the flow of performing analog-to-digital conversion on the sampled phase current signal of the compressor may be implemented by opening an a/D sampling port, interrupting the sampling port through a peripheral of the DSP, and continuously reading the sampled data, thereby implementing data acquisition of the phase current information. The DSP is provided with a peripheral interrupt extension module PIE, arbitrates peripheral or external pin interrupt request signals by setting a register in the PIE, and sends arbitration results to the CPU for processing. When the number of sampling points meets the requirement, finishing sampling; for example, a threshold value a is set, and when the sampling value x reaches a < x < -a, the sampling point data meets the requirement.
Step S130, performing wavelet analysis on the sampled compressor phase current signals to extract the liquid-carrying characteristics of the compressor in the compressor phase current signals.
Optionally, filtering processing may be performed on the sampled compressor phase current signal to filter out sampling interference in the compressor phase current signal, and performing wavelet analysis on the filtered compressor phase current signal.
Fig. 4 is a flowchart of an embodiment of the step of performing wavelet analysis on the sampled compressor phase current signals to extract the compressor liquid-carrying characteristics in the compressor phase current signals, as shown in fig. 4, and in some embodiments, the step S130 includes a step S131 and a step S132.
And S131, performing wavelet analysis on the sampled compressor phase current signals, and extracting the liquid-carrying characteristic frequency of the compressor.
FIG. 5 is a flowchart of an embodiment of a step of extracting a characteristic frequency of a compressor with liquid by performing wavelet analysis on a sampled phase current signal of the compressor. As shown in fig. 5, in some embodiments, step S131 includes step S1311, step S1312, and step S1313.
Step 1311, sets analysis parameters and analysis frequency band range required for wavelet analysis.
And step S1312, obtaining a wavelet basis function for performing wavelet analysis on the phase current signal of the compressor according to the set analysis parameters.
And step S1313, performing wavelet packet decomposition of a target order on the sampled compressor phase current signals according to the obtained wavelet basis functions and the analysis frequency range, and extracting the compressor liquid-carrying characteristic frequency.
The wavelet analysis technology belongs to one kind of signal time-frequency analysis, and can effectively detect local mutation signals. The wavelet analysis focuses on the details of the signal, has the characteristic of multi-resolution, has the capability of representing the local characteristics of the signal in both time-frequency domains, and is a time-frequency localization analysis method with changeable time windows and frequency windows. The wavelet transform has higher frequency resolution and lower time resolution in the low frequency part and higher time resolution and lower frequency resolution in the high frequency part, and is very suitable for detecting transient abnormal phenomena carried in normal signals and displaying the components of the transient abnormal phenomena.
The wavelet is a very small wave as its name implies, and has psi (t) E L2(R) Fourier transform thereof
If the following conditions are satisfied
In this case, ψ (t) is referred to as a wavelet basis, and the wavelet basis needs to be transformed during the analysis, so that the wavelet basis ψ (t) is obtained by introducing the scale factor a and the translation factor b:
the formula (3) is called as a wavelet basis function and is obtained by performing expansion and translation on the wavelet basis, and the time-frequency characteristic of the wavelet basis function can be changed by changing the values of the scale factor a and the translation factor b.
For an arbitrary input signal f (t) e L2(R) its continuous wavelet transform:
i.e. the inner product of f (t) and wavelet basis function is the result of wavelet transform, i.e. f (t) maps the time-frequency information to the time-frequency plane by wavelet transform, Wf(a, b) is a two-dimensional plane reflecting the signal at time b
The amplitude of the frequency location. Inverse transform of wavelet transform:
in the formula (5)
Are the inverse transform coefficients. Wavelet basis function ψ (t) generated due to wavelet basis ψ (t)a,b(t) acts as an observation window for the signal being analyzed in the wavelet transform, so ψ (t) should also satisfy the constraint of the general function:
therefore, the temperature of the molten metal is controlled,
is a continuous function. In order to satisfy the inverse transformation condition,
at the origin, 0 is required, i.e.
In order to achieve a numerically stable signal reconstruction, the fourier transformation of the wavelet basis ψ (t) is required to satisfy a stability condition in addition to the inverse transformation condition
In practical applications, it is usually required to implement on a digital controller, for example, in a DSP, the processor can only process discrete signals, and the continuous wavelet transform cannot be applied therein, so that the continuous wavelet transform needs to be discretized. The dispersion of the continuous wavelet is for a scale factor a and a translation factor b, and is generally desirable
Where j ∈ Z assumes a0If > 1, the corresponding discrete wavelet basis function is:
the phase current characteristic of the compressor when liquid is carried is a non-stationary time-varying signal and has the characteristic of a local pulse signal. Meanwhile, the rotor compressor is a power machine which intermittently completes working cycle, and the work of the rotor compressor has the characteristic of periodicity. The amplitude and frequency of the phase current of the compressor are two important parameters for measuring the phase current characteristics, and in order to extract the phase current liquid-carrying characteristics by using discrete wavelet transformation, whether the amplitude and frequency change conditions of the analyzed signals can be correctly reflected by the result of the discrete wavelet analysis needs to be analyzed, namely whether the wavelet coefficient after wavelet transformation can correctly reflect the relationship between the amplitude and the frequency of the signals.
Research shows that after wavelet transformation is carried out on test signals with the same frequency and different amplitudes, the wavelet coefficients can correctly reflect the change situation of the amplitudes. But after wavelet transform, the higher the frequency of the test signal with the same amplitude, the smaller the wavelet coefficient. In practical applications, the frequency components making up the signal are different, and the higher frequency components are attenuated more after transformation. Although the transformation does not affect the reconstruction of the wavelet decomposed signal, the size of the transformed wavelet coefficient cannot truly reflect the strength of each frequency component in the signal. For phase current characteristic identification by using the discrete wavelet transform result, a signal reconstruction process is not needed, and the amplitude of a target signal is needed. Therefore, in the phase current liquid-carrying characteristic extraction, the analysis result is not reasonable and accurate enough, and the wavelet base needs to be improved.
By analyzing the factors influencing the analysis result, the attenuation factor existing in discrete wavelet transformation needs to be eliminated for improvement, so that the amplitude characteristic of each frequency component in the phase current signal can be correctly reflected by the size of the transformed wavelet coefficient.
The discrete wavelet basis function obtained from equation (9) is modified as:
where f is a signal frequency corresponding to the scale factor a of the wavelet transform, and γ is a correction coefficient. Attenuation component and frequencyIn relation to, and thus introducing a frequency term
And then multiplying by a coefficient gamma to debug and correct the actual transformation result, thereby realizing the offset of the attenuation components. The gamma value can be determined after debugging through actual test. By improving the wavelet basis function, the attenuation component can be offset, and the precision and the accuracy of feature extraction are improved.
The analysis parameters may specifically include a scale factor a, a shift factor b, and a signal frequency f corresponding to the scale factor a of the wavelet transform. And setting a scale factor a, a translation factor b and a signal frequency f corresponding to the scale factor a of the wavelet transformation according to actual requirements, and obtaining a corresponding wavelet basis function according to the formula (10), so that the phase current signals obtained by sampling are subjected to wavelet packet analysis through the obtained wavelet basis function.
The analysis frequency range can be set according to actual requirements, and if the analysis frequency range is too small and the CPU computing capability cannot keep up with the analysis frequency range, the waveform is distorted; if the analysis frequency range is too large, the extraction of the characteristic value is not facilitated, namely, the incorrect characteristic value is extracted.
The sampled compressor phase current signals are subjected to decomposition calculation of high-frequency and low-frequency components, so that the original compressor phase current signals are decomposed into a plurality of wavelet packets containing compressor liquid-carrying characteristics (frequencies), and the positions with the most obvious liquid-carrying characteristic frequencies are extracted from the decomposition orders of the wavelet packets subjected to multi-layer decomposition.
Because complete wavelet packet binary tree decomposition needs a large amount of computing resources and generates data redundancy, the invention adopts incomplete wavelet tree decomposition to avoid some problems caused by the data redundancy, save the computing resources and ensure the real-time property. The basic idea is that, for a digital signal, the sampling frequency is set to be f, and the effective frequency range is 0-f/2 according to the sampling theorem. If the signal is subjected to complete wavelet packet binary tree decomposition, each node corresponds to a frequency band, and two nodes of the next order generated by one node decomposition are equivalent to the frequency band bisection of the original node. As shown in the figureAnd 6, a binary tree decomposition diagram of a 3-layer complete wavelet packet is shown, wherein the lower subscript of S represents the order, and the upper subscript represents the position of the wavelet packet on the layer. S0 0Representing the acquired original signal, the original signal is decomposed into a plurality of wavelet packets containing information characteristics by performing decomposition calculation of high-frequency and low-frequency components based on wavelet analysis on the original signal. By adopting the method, the position of the target information can be positioned by performing pre-analysis, and the effect of saving computing resources is realized.
Given a specific frequency fs of interest (ensuring that within the range of the effective band, the target frequency is characteristic of the presence of fluid) and the required band width Fw, a node D must be present, satisfying the following conditions:
fs is in the frequency range corresponding to D; the frequency band width corresponding to D is less than Fw, and the frequency band width of the father node of D is more than or equal to Fw. It can be seen that D is a wavelet packet tree node containing information of a specific frequency band, and analysis of information at and near a specific frequency will effectively increase the analysis speed.
And S132, carrying out Fourier transform on the basis of the extracted compressor liquid-carrying characteristic frequency to obtain an amplitude corresponding to the compressor liquid-carrying characteristic frequency.
Although the fast Fourier transform has no time resolution, the analysis result of each frequency point can be obtained, so that the amplitude of the liquid-carrying characteristic quantity of the compressor can be obtained through the fast Fourier transform, and the suction dryness of the compressor can be judged. For example, wavelet analysis is firstly carried out on the digital signals of the phase current of the compressor, the position with the most obvious characteristic frequency of liquid is proposed in the wavelet packet decomposition order of the multi-layer decomposition, and then the fast Fourier transform is carried out.
Fig. 7 is a flow chart of data analysis of compressor phase current signals. As shown in fig. 7, before processing the collected compressor phase current data, analysis parameters, such as wavelet basis functions, analysis frequency range, and the like, need to be set, wavelet analysis is performed on the collected data, whether an analysis target order is reached is determined, and then FFT analysis is performed after the analysis target order is reached, so as to obtain an analysis result of the phase current characteristic quantity.
Further, the method may further include: and acquiring the suction dryness of the compressor according to the extracted compressor liquid-carrying characteristics in the phase current signal of the compressor.
After the liquid carrying characteristics of the compressor are extracted through wavelet analysis, the suction dryness of the compressor can be obtained according to the extracted liquid carrying characteristics of the compressor, and therefore after the suction dryness is obtained, the system is guaranteed to run in the optimal suction dryness state through controlling the running frequency of the compressor and the opening of the electronic expansion valve, and the compressor can work maximally.
In some embodiments, the dryness of suction of the compressor is determined based on the liquid-bearing characteristic and a given dryness of suction. Specifically, PID processing is carried out on the given inhalation dryness, and a PID processing result of the given inhalation dryness is obtained and is used as inhalation dryness control information; and determining the suction dryness of the compressor by utilizing a pre-constructed optimal suction refrigerant control module according to the liquid-carrying characteristic information and the suction dryness control information. And the optimal suction refrigerant control (namely OSRQC) module comprehensively processes the extracted liquid-carrying characteristic information and the suction dryness control information to obtain the optimal suction dryness.
The OSRQC, namely the optimal suction refrigerant control module, plays a role in comprehensively controlling input information. The input quantities of the module include: and controlling the target value according to the compressor liquid-carrying characteristic quantity and the suction dryness extracted according to the compressor phase current.
The recognition result of the liquid-carrying characteristics of the phase current of the compressor and the suction dryness value calculated by the system parameters are integrated by adopting a weighting coefficient mode, and then the final suction dryness can be represented by the following formula (11):
S＝S1+σ(S1-S2) (11)
wherein, S1 represents the compressor suction dryness obtained by phase current liquid-carrying characteristic identification, S2 represents the suction dryness obtained by system parameter calculation, and σ represents the suction dryness deviation correction coefficient.
The main factors influencing the air suction dryness are the operation frequency of the compressor and the opening degree of the electronic expansion valve, so that the air suction dryness is still insufficient only by identifying, and related loads need to be controlled, namely, an optimal operation frequency and the opening degree of the electronic expansion valve are found by utilizing an optimization algorithm (the relationship between the frequency and the opening degree and the air suction dryness can be determined by constructing a value function), so that the optimal air suction dryness state is realized, and the compressor is in the maximum work state.
And fitting a calculation formula of the air suction dryness through experimental tests and theoretical calculation, wherein an optimal air suction dryness range exists in the correlation between the air suction dryness and the defrosting heat under all working conditions, and high defrosting heat supply is realized. And taking the suction dryness corresponding to the maximum defrosting heat as the target of system regulation.
Fitting calculation equation of inspiratory quality S2:
wherein: A. b, C, D, E is a constant coefficient; alpha, beta, chi and delta are weight coefficients; ts is the inspiratory temperature, unit: DEG C; td is the exhaust temperature, in units: DEG C; ps is the inspiratory pressure, unit: mpa; pd is the exhaust pressure, unit: mpa.
As for the compressor suction dryness S1 obtained by phase current liquid-carrying characteristic recognition, as shown in fig. 7, the program first performs wavelet analysis on the digital signal input to the data analysis module, and extracts the most significant position of the liquid-carrying characteristic frequency in the wavelet packet decomposition order of the multi-layer decomposition. And then, carrying out fast Fourier transform, wherein although the fast Fourier transform has no time resolution, the analysis result of each frequency point can be obtained. Therefore, the amplitude of the liquid-carrying characteristic quantity of the compressor can be obtained through fast Fourier transform, and the suction dryness of the compressor can be judged. The optimum dryness range for inspiration is for example: and S1 is 0.92-1.00, the amplitude of the corresponding compressor liquid-carrying characteristic quantity also has an optimal range, for example, a < ═ x < ═ b, and the optimal suction dryness is determined as long as the amplitude of the compressor liquid-carrying characteristic quantity is in the optimal range. The optimal range is set according to the motor parameters, the operation current, the frequency, the operation condition and the like of the compressor.
The extraction process of the phase current characteristics of the compressor can be automatically operated by the system, and the software architecture adopts a modular form. The system can be mainly divided into the following three modules: a main control module, a data acquisition module, and a data analysis module, and fig. 8 is a software functional structure diagram according to the present invention.
The main control module is responsible for dispatching and responding to all the modules in the overall situation, and all the modules are organically combined together to realize the function of extracting the liquid-carrying state characteristics of the compressor. And the main control module reads the running state of the system, and enters a next flow module for processing when recognizing that the system is in a defrosting mode. And the data acquisition module comprises a sampling control module and a filtering control module, and the sampling control module is responsible for sampling phase current of the compressor and controlling the sampling starting and stopping moments. The filtering control module carries out primary filtering on the acquired current signals and filters sampling interference in the phase current signals. And the data analysis module is used for performing wavelet analysis and FFT analysis according to the analysis flow set by the program, extracting the liquid-carrying characteristics of the phase current of the compressor and outputting the identification result of the suction dryness.
In some embodiments, the execution flow of the main control module is shown with reference to fig. 9.
First, whether the defrosting operation mode is adopted is detected. When the defrosting operation is carried out, the starting module is initialized (a main control program comprises a phase current characteristic extraction function, when the program calls the function, the initialization function is firstly entered, some variables are generally initialized in the function), the phase current of the compressor is subjected to sampling control and filtering control, the waveform to be analyzed is calculated through continuous iteration, an iteration period (namely an analysis period, a section of waveform is analyzed by wavelet analysis, analysis can be carried out only when a certain waveform period is reached, so that the extracted characteristic quantity is accurate) threshold value can be set in the program, after the threshold value is reached, the program enters the logic of the next stage, and when the analysis period is reached, analysis is carried out through analysis parameters, and an analysis result is output.
Fig. 10 is a block diagram illustrating an embodiment of a liquid-carrying characteristic extracting apparatus for a compressor of an air conditioner according to the present invention. As shown in fig. 10, the compressor fluid-carrying characteristic extraction apparatus 100 includes a determination unit 110, a sampling unit 120, and an acquisition unit 140 of an analysis unit 130.
The determination unit 110 is configured to determine whether the air conditioner is in a defrosting mode when the air conditioner is in a heating operation.
For example, when the air conditioner is in heating operation, the operation parameters of the air conditioner are obtained, and whether the air conditioner is in a defrosting mode or not can be determined according to the operation parameters of the air conditioner.
The sampling unit 120 is configured to sample a compressor phase current of the air conditioner to obtain a compressor phase current signal when the determining unit 110 determines that the air conditioner is in the defrosting mode.
For example, the compressor phase current may be sampled by a current sensor to obtain a compressor phase current signal.
Optionally, the apparatus 100 may further include a filtering unit (not shown), which may perform filtering processing on the sampled compressor phase current signal to filter out sampling interference in the compressor phase current signal.
Optionally, analog-to-digital conversion processing may be performed on the sampled compressor phase current signal to obtain a digital quantity of the compressor phase current. Referring to fig. 3, the flow of performing analog-to-digital conversion on the sampled phase current signal of the compressor may be implemented by opening an a/D sampling port, interrupting the sampling port through a peripheral of the DSP, and continuously reading the sampled data, thereby implementing data acquisition of the phase current information. The DSP is provided with a peripheral interrupt extension module PIE, arbitrates peripheral or external pin interrupt request signals by setting a register in the PIE, and sends arbitration results to the CPU for processing. When the number of sampling points meets the requirement, finishing sampling; for example, a threshold value a is set, and when the sampling value x reaches a < x < -a, the sampling point data meets the requirement.
The analyzing unit 130 is configured to perform wavelet analysis on the compressor phase current signal sampled by the sampling unit 120 to extract a compressor liquid-carrying characteristic in the compressor phase current signal.
Optionally, filtering processing may be performed on the sampled compressor phase current signal to filter out sampling interference in the compressor phase current signal, and performing wavelet analysis on the filtered compressor phase current signal.
Fig. 11 is a block diagram of a specific embodiment of an analysis unit according to the present invention. As shown in fig. 11, the analysis unit 130 includes a wavelet analysis subunit 131 and a fourier transform subunit 132.
And the wavelet analysis subunit 131 is configured to perform wavelet analysis on the sampled compressor phase current signal to extract a compressor liquid-carrying characteristic frequency.
Specifically, setting analysis parameters and an analysis frequency range required for wavelet analysis; obtaining a wavelet basis function for performing wavelet analysis on the phase current signal of the compressor according to set analysis parameters; and performing wavelet packet decomposition of a target order on the sampled compressor phase current signal according to the obtained wavelet basis function and the analysis frequency range, and extracting the compressor liquid-carrying characteristic frequency.
The wavelet analysis technology belongs to one kind of signal time-frequency analysis, and can effectively detect local mutation signals. The wavelet analysis focuses on the details of the signal, has the characteristic of multi-resolution, has the capability of representing the local characteristics of the signal in both time-frequency domains, and is a time-frequency localization analysis method with changeable time windows and frequency windows. The wavelet transform has higher frequency resolution and lower time resolution in the low frequency part and higher time resolution and lower frequency resolution in the high frequency part, and is very suitable for detecting transient abnormal phenomena carried in normal signals and displaying the components of the transient abnormal phenomena.
The wavelet is a very small wave as its name implies, and has psi (t) E L2(R) Fourier transform thereof
If the following conditions are satisfied
In this case, ψ (t) is referred to as a wavelet basis, and the wavelet basis needs to be transformed during the analysis, so that the wavelet basis ψ (t) is obtained by introducing the scale factor a and the translation factor b:
the formula (3) is called as a wavelet basis function and is obtained by performing expansion and translation on the wavelet basis, and the time-frequency characteristic of the wavelet basis function can be changed by changing the values of the scale factor a and the translation factor b.
For an arbitrary input signal f (t) e L2(R) its continuous wavelet transform:
i.e. the inner product of f (t) and wavelet basis function is the result of wavelet transform, i.e. f (t) maps the time-frequency information to the time-frequency plane by wavelet transform, Wf(a, b) is a two-dimensional plane reflecting the signal at time b
The amplitude of the frequency location. Inverse transform of wavelet transform:
in the formula (5)
Are the inverse transform coefficients. Wavelet basis function ψ (t) generated due to wavelet basis ψ (t)a,b(t) acts as an observation window for the signal being analyzed in the wavelet transform, so ψ (t) should also satisfy the constraint of the general function:
therefore, the temperature of the molten metal is controlled,
is a continuous function. In order to satisfy the inverse transformation condition,
at the origin, 0 is required, i.e.
In order to achieve a numerically stable signal reconstruction, the fourier transformation of the wavelet basis ψ (t) is required to satisfy a stability condition in addition to the inverse transformation condition
In practical applications, it is usually required to implement on a digital controller, for example, in a DSP, the processor can only process discrete signals, and the continuous wavelet transform cannot be applied therein, so that the continuous wavelet transform needs to be discretized. The dispersion of the continuous wavelet is for a scale factor a and a translation factor b, and is generally desirable
Where j ∈ Z assumes a0If > 1, the corresponding discrete wavelet basis function is:
the phase current characteristic of the compressor when liquid is carried is a non-stationary time-varying signal and has the characteristic of a local pulse signal. Meanwhile, the rotor compressor is a power machine which intermittently completes working cycle, and the work of the rotor compressor has the characteristic of periodicity. The amplitude and frequency of the phase current of the compressor are two important parameters for measuring the phase current characteristics, and in order to extract the phase current liquid-carrying characteristics by using discrete wavelet transformation, whether the amplitude and frequency change conditions of the analyzed signals can be correctly reflected by the result of the discrete wavelet analysis needs to be analyzed, namely whether the wavelet coefficient after wavelet transformation can correctly reflect the relationship between the amplitude and the frequency of the signals.
Research shows that after wavelet transformation is carried out on test signals with the same frequency and different amplitudes, the wavelet coefficients can correctly reflect the change situation of the amplitudes. But after wavelet transform, the higher the frequency of the test signal with the same amplitude, the smaller the wavelet coefficient. In practical applications, the frequency components making up the signal are different, and the higher frequency components are attenuated more after transformation. Although the transformation does not affect the reconstruction of the wavelet decomposed signal, the size of the transformed wavelet coefficient cannot truly reflect the strength of each frequency component in the signal. For phase current characteristic identification by using the discrete wavelet transform result, a signal reconstruction process is not needed, and the amplitude of a target signal is needed. Therefore, in the phase current liquid-carrying characteristic extraction, the analysis result is not reasonable and accurate enough, and the wavelet base needs to be improved.
By analyzing the factors influencing the analysis result, the attenuation factor existing in discrete wavelet transformation needs to be eliminated for improvement, so that the amplitude characteristic of each frequency component in the phase current signal can be correctly reflected by the size of the transformed wavelet coefficient.
The discrete wavelet basis function obtained from equation (9) is modified as:
where f is a signal frequency corresponding to the scale factor a of the wavelet transform, and γ is a correction coefficient. The attenuation component being frequency dependent, thus introducing a frequency term
And then multiplying by a coefficient gamma to debug and correct the actual transformation result, thereby realizing the offset of the attenuation components. The gamma value can be determined after debugging through actual test. By improving the wavelet basis function, the attenuation component can be offset, and the precision and the accuracy of feature extraction are improved.
The analysis parameters may specifically include a scale factor a, a shift factor b, and a signal frequency f corresponding to the scale factor a of the wavelet transform. And setting a scale factor a, a translation factor b and a signal frequency f corresponding to the scale factor a of the wavelet transformation according to actual requirements, and obtaining a corresponding wavelet basis function according to the formula (10), so that the phase current signals obtained by sampling are subjected to wavelet packet analysis through the obtained wavelet basis function.
The analysis frequency range can be set according to actual requirements, and if the analysis frequency range is too small and the CPU computing capability cannot keep up with the analysis frequency range, the waveform is distorted; if the analysis frequency range is too large, the extraction of the characteristic value is not facilitated, namely, the incorrect characteristic value is extracted.
The sampled compressor phase current signals are subjected to decomposition calculation of high-frequency and low-frequency components, so that the original compressor phase current signals are decomposed into a plurality of wavelet packets containing compressor liquid-carrying characteristics (frequencies), and the positions with the most obvious liquid-carrying characteristic frequencies are extracted from the decomposition orders of the wavelet packets subjected to multi-layer decomposition.
Because complete wavelet packet binary tree decomposition needs a large amount of computing resources and generates data redundancy, the invention adopts incomplete wavelet tree decomposition to avoid some problems caused by the data redundancy, save the computing resources and ensure the real-time property. The basic idea is that, for a digital signal, the sampling frequency is set to be f, and the effective frequency range is 0-f/2 according to the sampling theorem. If the signal is subjected to complete wavelet packet binary tree decomposition, each node corresponds to a frequency band, and two nodes of the next order generated by one node decomposition are equivalent to the frequency band bisection of the original node. As shown in fig. 6, it is a schematic diagram of a binary tree decomposition of a 3-layer complete wavelet packet, where the lower subscript of S represents the order, and the upper subscript represents the position of the wavelet packet in the layer. S0 0Representing the acquired original signal, the original signal is decomposed into a plurality of wavelet packets containing information characteristics by performing decomposition calculation of high-frequency and low-frequency components based on wavelet analysis on the original signal. By adopting the method, the position of the target information can be positioned by performing pre-analysis, and the effect of saving computing resources is realized.
Given a specific frequency fs of interest (ensuring that within the range of the effective band, the target frequency is characteristic of the presence of fluid) and the required band width Fw, a node D must be present, satisfying the following conditions:
fs is in the frequency range corresponding to D; the frequency band width corresponding to D is less than Fw, and the frequency band width of the father node of D is more than or equal to Fw. It can be seen that D is a wavelet packet tree node containing information of a specific frequency band, and analysis of information at and near a specific frequency will effectively increase the analysis speed.
And the fourier transform subunit 132 is configured to perform fourier transform on the extracted compressor liquid-carrying characteristic frequency to obtain an amplitude corresponding to the compressor liquid-carrying characteristic frequency.
Optionally, a fast fourier transform is adopted, and although the fast fourier transform has no time resolution, an analysis result of each frequency point can be obtained, so that the amplitude of the liquid-carrying characteristic quantity of the compressor can be obtained through the fast fourier transform, and the dryness of the suction gas of the compressor can be judged. For example, wavelet analysis is firstly carried out on the digital signals of the phase current of the compressor, the position with the most obvious characteristic frequency of liquid is proposed in the wavelet packet decomposition order of the multi-layer decomposition, and then the fast Fourier transform is carried out.
Fig. 7 is a flow chart of data analysis of compressor phase current signals. As shown in fig. 7, before processing the collected compressor phase current data, analysis parameters, such as wavelet basis functions, analysis frequency range, and the like, need to be set, wavelet analysis is performed on the collected data, whether an analysis target order is reached is determined, and then FFT analysis is performed after the analysis target order is reached, so as to obtain an analysis result of the phase current characteristic quantity.
Further, the apparatus may further include: the obtaining unit 140 is configured to obtain the dryness of the sucked gas of the compressor according to the extracted liquid-carrying characteristics of the compressor in the phase current signal of the compressor.
After the liquid carrying characteristics of the compressor are extracted through wavelet analysis, the suction dryness of the compressor can be obtained according to the extracted liquid carrying characteristics of the compressor, and therefore after the suction dryness is obtained, the system is guaranteed to run in the optimal suction dryness state through controlling the running frequency of the compressor and the opening of the electronic expansion valve, and the compressor can work maximally.
In some embodiments, the dryness of suction of the compressor is determined based on the liquid-bearing characteristic and a given dryness of suction. Specifically, PID processing is carried out on the given inhalation dryness, and a PID processing result of the given inhalation dryness is obtained and is used as inhalation dryness control information; and determining the suction dryness of the compressor by utilizing a pre-constructed optimal suction refrigerant control module according to the liquid-carrying characteristic information and the suction dryness control information. And the optimal suction refrigerant control (namely OSRQC) module comprehensively processes the extracted liquid-carrying characteristic information and the suction dryness control information to obtain the optimal suction dryness.
The OSRQC, namely the optimal suction refrigerant control module, plays a role in comprehensively controlling input information. The input quantities of the module include: and controlling the target value according to the compressor liquid-carrying characteristic quantity and the suction dryness extracted according to the compressor phase current.
The recognition result of the liquid-carrying characteristics of the phase current of the compressor and the suction dryness value calculated by the system parameters are integrated by adopting a weighting coefficient mode, and then the final suction dryness can be represented by the following formula (11):
S＝S1+σ(S1-S2) (11)
wherein, S1 represents the compressor suction dryness obtained by phase current liquid-carrying characteristic identification, S2 represents the suction dryness obtained by system parameter calculation, and σ represents the suction dryness deviation correction coefficient.
The main factors influencing the air suction dryness are the operation frequency of the compressor and the opening degree of the electronic expansion valve, so that the air suction dryness is still insufficient only by identifying, and related loads need to be controlled, namely, an optimal operation frequency and the opening degree of the electronic expansion valve are found by utilizing an optimization algorithm (the relationship between the frequency and the opening degree and the air suction dryness can be determined by constructing a value function), so that the optimal air suction dryness state is realized, and the compressor is in the maximum work state.
And fitting a calculation formula of the air suction dryness through experimental tests and theoretical calculation, wherein an optimal air suction dryness range exists in the correlation between the air suction dryness and the defrosting heat under all working conditions, and high defrosting heat supply is realized. And taking the suction dryness corresponding to the maximum defrosting heat as the target of system regulation.
Fitting and calculating equation of inspiration dryness:
wherein: A. b, C, D, E is a constant coefficient; alpha, beta, chi and delta are weight coefficients; ts is the inspiratory temperature, unit: DEG C; td is the exhaust temperature, in units: DEG C; ps is the inspiratory pressure, unit: mpa; pd is the exhaust pressure, unit: mpa.
As for the compressor suction dryness S1 obtained by phase current liquid-carrying characteristic recognition, as shown in fig. 7, the program first performs wavelet analysis on the digital signal input to the data analysis module, and extracts the most significant position of the liquid-carrying characteristic frequency in the wavelet packet decomposition order of the multi-layer decomposition. And then, carrying out fast Fourier transform, wherein although the fast Fourier transform has no time resolution, the analysis result of each frequency point can be obtained. Therefore, the amplitude of the liquid-carrying characteristic quantity of the compressor can be obtained through fast Fourier transform, and the suction dryness of the compressor can be judged. The optimum dryness range for inspiration is for example: and S1 is 0.92-1.00, the amplitude of the corresponding compressor liquid-carrying characteristic quantity also has an optimal range, for example, a < ═ x < ═ b, and the optimal suction dryness is determined as long as the amplitude of the compressor liquid-carrying characteristic quantity is in the optimal range. The optimal range is set according to the motor parameters, the operation current, the frequency, the operation condition and the like of the compressor.
Fig. 12 is a schematic structural diagram of an embodiment of a high defrosting heat control device based on multi-dimensional coupling.
In the example shown in fig. 12, the motor control system employs a motor drive control scheme based on a position sensorless. In the motor control system, the motor can be controlled through a comparator, a proportional integral regulating module (PI regulator), a space vector pulse width modulation module (SVPWM module) and a coordinate change module (Clark-Park module) based on a given d-axis current Id, a given rotating speed Wr, an observed rotating speed Wr observed by a rotating speed observation module and a feedback rotating speed delta Wr fed back by an optimal suction refrigerant control (OSRQC) module. Wherein, the clark transformation transforms abc into a stationary alpha and beta coordinate system. And the Park transformation is to transform abc into a rotating dqdq coordinate system.
In the example shown in fig. 12, the control load is a compressor and an electronic expansion valve. The motor in the compressor can adopt a Permanent Magnet Synchronous Motor (PMSM).
In the example shown in fig. 12, the suction dryness recognition and control module is constructed on the basis of a motor control system, and mainly comprises a liquid-carrying characteristic quantity extraction module, a suction dryness PID control module, and an optimal suction refrigerant control (i.e., OSRQC) module. The optimal suction refrigerant control (namely OSRQC) module is responsible for comprehensively processing the extracted liquid-carrying state characteristic quantity and suction dryness control information, and three functions of identifying the liquid-carrying state of the compressor, controlling the suction dryness and optimizing an optimal suction dryness working point on line are realized. The normal waveform and the compressor phase current waveform in a slight liquid-carrying state have a slight phase shift, a liquid-carrying state characteristic quantity of a section of phase current waveform is extracted through a wavelet analysis method, then the characteristic quantity is transmitted to an OSRQC module as shown in fig. 12, the optimal frequency and the valve opening degree are obtained through optimization by combining with suction dryness control information, and the optimal suction dryness is calculated by utilizing a value function, so that the identification of the liquid-carrying state of the compressor and the control of the suction dryness are realized.
The input parameters of the Clark-Park module are processed by an analog-to-digital conversion module (namely, an A/D module), a Wavelet Analysis module (namely, Wavelet Analysis) and a characteristic quantity extraction module to obtain a first characteristic S1, and the first characteristic S1 is input to a first input end of the OSRQC module. ControlAnd the parameters of the load making output are processed by the A/D module to obtain a second characteristic S2, and the second characteristic S2 is input to a second input end of the OSRQC module. And the given inspiratory quality S is input to a third input end of the OSRQC module after passing through the PID module. And the first output end of the OSRQC module is connected to the electronic expansion valve. And a second output end of the OSRQC module outputs feedback rotating speed delta Wr. And a third output end of the OSRQC module is output to a comparator after passing through a calculation module of the feedback inhalation dryness S, and the comparator compares the given inhalation dryness S with the feedback inhalation dryness S and outputs the comparison result to a PID module. A module for calculating the feedback inhalation dryness S, specifically calculating the feedback inhalation dryness S ═ S1+σ(S1-S2)。
The invention also provides a storage medium corresponding to the method for extracting the liquid-carrying characteristics of the compressor, wherein a computer program is stored on the storage medium, and the computer program is used for realizing the steps of any one of the methods when being executed by a processor.
The invention also provides an air conditioner corresponding to the method for extracting the liquid-carrying characteristics of the compressor, which comprises a processor, a memory and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of any one of the methods when executing the program.
The invention also provides an air conditioner corresponding to the compressor liquid-carrying characteristic extraction device, which comprises the compressor liquid-carrying characteristic extraction device.
Accordingly, the scheme provided by the invention extracts the liquid-carrying characteristic quantity of the compressor based on the wavelet analysis technology, so as to identify the suction dryness of the compressor; an analysis method suitable for extracting liquid-carrying characteristics of phase current of the compressor is constructed, online recognition of the suction dryness of the compressor is realized, and a technical basis is provided for realizing optimal suction dryness control. The method is based on the wavelet analysis technology, fully considers the liquid-carrying characteristic characteristics of phase current, improves the wavelet basis function, and has better information extraction capability.
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the invention and the following claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or a combination of any of these. In addition, each functional unit may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and the parts serving as the control device may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. A method for extracting liquid-carrying characteristics of a compressor of an air conditioner is characterized by comprising the following steps:
determining whether the air conditioner is in a defrosting mode when the air conditioner is in heating operation;
when the air conditioner is determined to be in the defrosting mode, sampling the phase current of the compressor of the air conditioner to obtain a phase current signal of the compressor;
performing wavelet analysis on the sampled compressor phase current signals to extract the liquid-carrying characteristics of the compressor in the compressor phase current signals;
wavelet analysis is carried out on the sampled compressor phase current signals, and the wavelet analysis comprises the following steps:
setting analysis parameters and analysis frequency range required by wavelet analysis;
obtaining a wavelet basis function for performing wavelet analysis on the phase current signal of the compressor according to set analysis parameters;
performing wavelet packet decomposition of a target order on the sampled compressor phase current signal according to the obtained wavelet basis function and the analysis frequency range, and extracting the compressor liquid-carrying characteristic frequency;
the wavelet basis functions comprise:
wherein a is a scale factor, b is a translation factor,
f is the signal frequency corresponding to the scale factor a of the wavelet transform, j belongs to Z, a0>1 and gamma is a correction coefficient.
2. The method of claim 1, further comprising:
filtering the sampled compressor phase current signals to filter out sampling interference in the compressor phase current signals;
wavelet analysis is carried out on the sampled compressor phase current signals, and the wavelet analysis comprises the following steps: and performing wavelet analysis on the compressor phase current signal obtained after filtering.
3. The method of claim 1 or 2, wherein performing wavelet analysis on the sampled compressor phase current signals to extract fluid-carrying features in the compressor phase current signals comprises:
wavelet analysis is carried out on the sampled phase current signals of the compressor, and the characteristic frequency of the compressor with liquid is extracted;
and carrying out Fourier transform based on the extracted compressor liquid-carrying characteristic frequency to obtain an amplitude corresponding to the compressor liquid-carrying characteristic frequency.
4. The utility model provides a compressor of air conditioner takes liquid characteristic extraction element which characterized in that includes:
a determination unit for determining whether the air conditioner is in a defrosting mode when the air conditioner is in a heating operation;
the sampling unit is used for sampling the phase current of the compressor of the air conditioner to obtain a phase current signal of the compressor when the determining unit determines that the air conditioner is in the defrosting mode;
the analysis unit is used for performing wavelet analysis on the compressor phase current signals sampled by the sampling unit so as to extract the liquid-carrying characteristics of the compressor in the compressor phase current signals;
the wavelet analysis subunit performs wavelet analysis on the sampled compressor phase current signal, and the wavelet analysis subunit includes:
setting analysis parameters and analysis frequency range required by wavelet analysis;
determining a wavelet basis function for performing wavelet analysis on the phase current signal of the compressor according to the set analysis parameters;
performing wavelet packet decomposition of a target order on the sampled compressor phase current signal according to the obtained wavelet basis function and the analysis frequency range, and extracting the compressor liquid-carrying characteristic frequency;
the wavelet basis functions comprise:
wherein a is a scale factor, b is a translation factor,
f is the signal frequency corresponding to the scale factor a of the wavelet transform, j belongs to Z, a0>1 and gamma is a correction coefficient.
5. The apparatus of claim 4, further comprising:
the filtering unit is used for filtering the sampled compressor phase current signals so as to filter out sampling interference in the compressor phase current signals;
the analysis unit is used for performing wavelet analysis on the sampled compressor phase current signals and comprises the following steps: and performing wavelet analysis on the compressor phase current signal obtained after filtering.
6. The apparatus according to claim 4 or 5, wherein the analyzing unit performs wavelet analysis on the sampled compressor phase current signal to extract a fluid-carrying feature in the compressor phase current signal, and comprises:
the wavelet analysis subunit is used for performing wavelet analysis on the sampled compressor phase current signals and extracting the compressor liquid-carrying characteristic frequency;
and the Fourier transform subunit is used for carrying out Fourier transform on the extracted compressor liquid-carrying characteristic frequency to obtain an amplitude corresponding to the compressor liquid-carrying characteristic frequency.
7. A storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 3.
8. An air conditioner comprising a processor, a memory, and a computer program stored in the memory and operable on the processor, wherein the processor executes the program to perform the steps of the method of any one of claims 1 to 3, and wherein the compressor fluid-carrying characteristic extraction apparatus of any one of claims 4 to 6 is included.
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| CN104481881A (en) * | 2014-10-27 | 2015-04-01 | 珠海格力电器股份有限公司 | Air-conditioning unit compressor liquid impact determination method and system and air-conditioning unit |
| CN110779265A (en) * | 2019-11-19 | 2020-02-11 | 浙江工业大学 | Remote monitoring system and method for air source heat pump defrosting based on fan current |
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